Yong Zhao
0000-0002-6762-266X
30 papers found
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Deep Learning-Based Prediction of Contact Maps and Crystal Structures of Inorganic Materials
Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints
Physics guided deep learning for generative design of crystal materials with symmetry constraints
Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
Data-driven deep generative design of stable spintronic materials
Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network
Generative design of stable semiconductor materials using deep learning and density functional theory
DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction
Generative Design of Stable Semiconductor Materials Using Deep Learning and DFT
High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
Active-Learning-Based Generative Design for the Discovery of Wide-Band-Gap Materials
Computational Discovery of New 2D Materials Using Deep Learning Generative Models
Deep learning pan‐specific model for interpretable MHC‐I peptide binding prediction with improved attention mechanism
Active learning based generative design for discovery of wide band gap materials
Semi-supervised teacher-student deep neural network for materials discovery
AlphaCrystal: Contact map based crystal structure prediction using deep learning
Physics guided deep learning generative models for crystal materials discovery
Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning
Machine Learning based prediction of noncentrosymmetric crystal materials
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
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